"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import torch
import torch.nn as nn
from functools import partial
import torch.nn.functional as F

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .layers import DropPath, to_2tuple, trunc_normal_, PatchEmbed, Mlp
from .registry import register_model
from .vision_transformer_hybrid import HybridEmbed

import torch
import torch.nn as nn


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
        'first_conv': 'patch_embed.proj', 'classifier': 'head',
        **kwargs
    }


default_cfgs = {
    # ConViT
    'convit_tiny': _cfg(
        url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"),
    'convit_small': _cfg(
        url="https://dl.fbaipublicfiles.com/convit/convit_small.pth"),
    'convit_base': _cfg(
        url="https://dl.fbaipublicfiles.com/convit/convit_base.pth")
}


class GPSA(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.,
                 locality_strength=1.):
        super().__init__()
        self.num_heads = num_heads
        self.dim = dim
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5
        self.locality_strength = locality_strength

        self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.v = nn.Linear(dim, dim, bias=qkv_bias)

        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.pos_proj = nn.Linear(3, num_heads)
        self.proj_drop = nn.Dropout(proj_drop)
        self.gating_param = nn.Parameter(torch.ones(self.num_heads))
        self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3)  # silly torchscript hack, won't work with None

    def forward(self, x):
        B, N, C = x.shape
        if self.rel_indices is None or self.rel_indices.shape[1] != N:
            self.rel_indices = self.get_rel_indices(N)
        attn = self.get_attention(x)
        v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def get_attention(self, x):
        B, N, C = x.shape
        qk = self.qk(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k = qk[0], qk[1]
        pos_score = self.rel_indices.expand(B, -1, -1, -1)
        pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2)
        patch_score = (q @ k.transpose(-2, -1)) * self.scale
        patch_score = patch_score.softmax(dim=-1)
        pos_score = pos_score.softmax(dim=-1)

        gating = self.gating_param.view(1, -1, 1, 1)
        attn = (1. - torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
        attn /= attn.sum(dim=-1).unsqueeze(-1)
        attn = self.attn_drop(attn)
        return attn

    def get_attention_map(self, x, return_map=False):
        attn_map = self.get_attention(x).mean(0)  # average over batch
        distances = self.rel_indices.squeeze()[:, :, -1] ** .5
        dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / distances.size(0)
        if return_map:
            return dist, attn_map
        else:
            return dist

    def local_init(self):
        self.v.weight.data.copy_(torch.eye(self.dim))
        locality_distance = 1  # max(1,1/locality_strength**.5)

        kernel_size = int(self.num_heads ** .5)
        center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
        for h1 in range(kernel_size):
            for h2 in range(kernel_size):
                position = h1 + kernel_size * h2
                self.pos_proj.weight.data[position, 2] = -1
                self.pos_proj.weight.data[position, 1] = 2 * (h1 - center) * locality_distance
                self.pos_proj.weight.data[position, 0] = 2 * (h2 - center) * locality_distance
        self.pos_proj.weight.data *= self.locality_strength

    def get_rel_indices(self, num_patches: int) -> torch.Tensor:
        img_size = int(num_patches ** .5)
        rel_indices = torch.zeros(1, num_patches, num_patches, 3)
        ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
        indx = ind.repeat(img_size, img_size)
        indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
        indd = indx ** 2 + indy ** 2
        rel_indices[:, :, :, 2] = indd.unsqueeze(0)
        rel_indices[:, :, :, 1] = indy.unsqueeze(0)
        rel_indices[:, :, :, 0] = indx.unsqueeze(0)
        device = self.qk.weight.device
        return rel_indices.to(device)


class MHSA(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def get_attention_map(self, x, return_map=False):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        attn_map = (q @ k.transpose(-2, -1)) * self.scale
        attn_map = attn_map.softmax(dim=-1).mean(0)

        img_size = int(N ** .5)
        ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
        indx = ind.repeat(img_size, img_size)
        indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
        indd = indx ** 2 + indy ** 2
        distances = indd ** .5
        distances = distances.to('cuda')

        dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / N
        if return_map:
            return dist, attn_map
        else:
            return dist

    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_gpsa=True, **kwargs):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.use_gpsa = use_gpsa
        if self.use_gpsa:
            self.attn = GPSA(
                dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, **kwargs)
        else:
            self.attn = MHSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class ConViT(nn.Module):
    """ Vision Transformer with support for patch or hybrid CNN input stage
    """

    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.,
                 drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm, global_pool=None,
                 local_up_to_layer=3, locality_strength=1., use_pos_embed=True):
        super().__init__()
        embed_dim *= num_heads
        self.num_classes = num_classes
        self.local_up_to_layer = local_up_to_layer
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.locality_strength = locality_strength
        self.use_pos_embed = use_pos_embed

        if hybrid_backbone is not None:
            self.patch_embed = HybridEmbed(
                hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim)
        else:
            self.patch_embed = PatchEmbed(
                img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches
        self.num_patches = num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        if self.use_pos_embed:
            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
            trunc_normal_(self.pos_embed, std=.02)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                use_gpsa=True,
                locality_strength=locality_strength)
            if i < local_up_to_layer else
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                use_gpsa=False)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)

        # Classifier head
        self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()

        trunc_normal_(self.cls_token, std=.02)
        self.apply(self._init_weights)
        for n, m in self.named_modules():
            if hasattr(m, 'local_init'):
                m.local_init()

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed', 'cls_token'}

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)

        if self.use_pos_embed:
            x = x + self.pos_embed
        x = self.pos_drop(x)

        for u, blk in enumerate(self.blocks):
            if u == self.local_up_to_layer:
                x = torch.cat((cls_tokens, x), dim=1)
            x = blk(x)

        x = self.norm(x)
        return x[:, 0]

    def forward(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x


def _create_convit(variant, pretrained=False, **kwargs):
    if kwargs.get('features_only', None):
        raise RuntimeError('features_only not implemented for Vision Transformer models.')

    return build_model_with_cfg(
        ConViT, variant, pretrained,
        default_cfg=default_cfgs[variant],
        **kwargs)


@register_model
def convit_tiny(pretrained=False, **kwargs):
    model_args = dict(
        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
        num_heads=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model = _create_convit(variant='convit_tiny', pretrained=pretrained, **model_args)
    return model


@register_model
def convit_small(pretrained=False, **kwargs):
    model_args = dict(
        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
        num_heads=9, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model = _create_convit(variant='convit_small', pretrained=pretrained, **model_args)
    return model


@register_model
def convit_base(pretrained=False, **kwargs):
    model_args = dict(
        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
        num_heads=16, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model = _create_convit(variant='convit_base', pretrained=pretrained, **model_args)
    return model
